• Journal of Korean Society of Rural Planning
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• v.8 no.1 s.15
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• pp.94-104
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• 2002

In spite of the importance of the farm area in Seoul, in providing fresh vegetables, a pleasant environment and a good quality of life for residents, rapid urbanization and industrialization have greatly reduced the farm area. The purpose of this study is to examine farmers' intentions and attitudes to provide supporting data for planning the strategy of urban agricultural development. All the collected data was analyzed using the contingency tables and the Chi-square test using the SAS computer statistical package. Based on analysis of the survey data, the leaseholders were found to be more satisfied with their job than the landowning farmers. Also, the small-scale farmers with green houses showed greater job satisfaction than the ordinary large-scale farmers. Farmers' views on the farming in Seoul were different depending on their status. Therefore, agricultural strategies in there should be considered their different attitudes.

• Atmosphere
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• v.28 no.4
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• pp.457-467
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• 2018

This study investigated the variability of spring (March-May) forest fire risk in Korea for the period 1991~2017 and analyzed its relationship with large-scale climate factors. The Forest Weather Index (FWI) representing the meteorological risk for forest fire occurrences calculated based on observational data and its relationship with large-scale climate factors were analyzed. We performed the empirical orthogonal function (EOF) analysis on the spring FWI. The leading EOF mode of FWI accounting for about 70% of total variability was found to be highly correlated with total number of forest fire occurrences in Korea. The high FWI, forest fire occurrence risk, in Korea, is associated with warmer atmosphere temperature in midwest Eurasia-China-Korea peninsula, cyclonic circulation anomaly in northeastern China-Korea peninsula-northwest pacific, westerly wind anomaly in central China-Korea peninsula, and low humidity in Korea. These are further related with warmer sea surface temperature and enhanced outgoing longwave radiation over Western Pacific, which represents a typical condition for a La $Ni\tilde{n}a$ episode. This suggests that large-scale climate factors over East Asia and ENSO could have a significant influence on the occurrence of spring forest fires in Korea.

• Journal of Navigation and Port Research
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• v.29 no.3 s.99
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• pp.215-220
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• 2005

This paper aim, to measure and evaluates the technical efficiency, pure technical efficiency and scale efficiency with two inputs and four outputs with the use of DEA(Data Envelopment Analysis) in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03{\%}$ and pure technical efficiency $96.80{\%}$ is much large then scale efficiency $93.83{\%}$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale)is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC finally, inefficiency RCC/RSC have to benchmarking with reference sets.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.11-17
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• 2022

Data mining is the task of accessing useful information from a large capacity of data. It can also be referred to as searching for correlations that can provide clues about the future in large data warehouses by using computer algorithms. It has been used in the tourism field for marketing, analysis, and business improvement purposes. This study aims to analyze the tourist profile in Turkey through data mining methods. The reason relies behind the selection of Turkey is the fact that Turkey welcomes millions of tourist every year which can be a role model for other touristic countries. In this study, an anonymous and large-scale data set was used under the law on the protection of personal data. The dataset was taken from a leading tourism company that is still active in Turkey. By using the k-means clustering algorithm on this data, key parameters of profiles were obtained and people were clustered into groups according to their characteristics. According to the outcomes, distinguishing characteristics are gathered under three main titles. These are the age of the tourists, the frequency of their vacations and the period between the reservation and the vacation itself. The results obtained show that the frequency of tourist vacations, the time between bookings and vacations, and age are the most important and characteristic parameters for a tourist's profile. Finally, planning future investments, events and campaign packages can make tourism companies more competitive and improve quality of service. For both businesses and tourists, it is advantageous to prepare individual events and offers for the three major groups of tourists.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.165-166
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• 2019

Big data analysis is the ability to collect, store, manage and analyze data from existing database management tools. Big data refers to large scale data that is generated in a digital environment, is large in size, has a short generation cycle, and includes not only numeric data but also text and image data. Big data is data that is difficult to manage and analyze in the conventional way. It has huge size, various types, fast generation and velocity. Therefore, companies in most industries are making efforts to create value through the application of Big data. In this study, we analyzed the meaning of keyword using Social Matrix, a big data analysis tool of Daum communications. Also, the theoretical implications are presented based on the analysis results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.97-104
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• 1999

Digital elevation models(DEMs) represent an important data base for orthophoto generation The quality of a DEM depends on the geometrical accuracy of the original point or line data. This study analyzes the effects of grid space and scanning resolution in DEM creation with image matching method. The less standard deviation of DEM error was introduced when we adopted small grid space, but no effects in scanning resolution. Based on the bias error analysis of the DEM, we found that the error of a large scale of aerial photograph was bigger than that of a small scale case, and that such error mainly came from the closed area in large scale photographs. In order to reduce the closed area, the experiment has been conducted using multi scale and different overlap of aerial photo images. The result shows that the size of closed area and the shaded area has been dramatically decreased due to the adoption of multi scale aerial images instead of a couple of stereo images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1150-1156
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• 2008

This paper introduces the PC clustering of the SIMD structure for a distributed processing of on-line contingency to assess a static security of a power system. To execute on-line contingency analysis of a large-scale power system, we need to use high-speed execution device. Therefore, we constructed PC-cluster system using PC clustering method of the SIMD structure and applied to a power system, which relatively shows high quality on the high-speed execution and has a low price. SIMD(single instruction stream, multiple data stream) is a structure that processes are controlled by one signal. The PC cluster system is consisting of 8 PCs. Each PC employs the 2 GHz Pentium 4 CPU and is connected with the others through ethernet switch based fast ethernet. Also, we consider N-1 line contingency that have high potentiality of occurrence realistically. We propose the distributed process algorithm of the SIMD structure for reducing too much execution time on the on-line N-1 line contingency analysis in the large-scale power system. And we have verified a usefulness of the proposed algorithm and the constructed PC cluster system through IEEE 39 and 118 bus system.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.196-209
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• 2022

Driving dynamic characteristics of semi-trailer loaded with precise equipments are very important to protect them from vibration, impact or other disturbances. In this paper, in order to identify the driving dynamic characteristics of the large scale semi-trailer equipped with swivel axle and hydropneumatic suspension unit, Dynamics Modeling & Simulation(M&S) were performed using general Dynamics Analysis Program(RecurDyn V9R2). The semi-trailer was modeled as two types - one is Multi Rigid Body Dynamics(MRBD) model, and the other Rigid-Flexible Body Dynamics(RFlex) one. The natural vibration mode and frequencies of semi-trailer body, acceleration of dummy-weight, pitch, roll and yaw of dummy-weight, swivel axle and hydropneumatic suspension cylinder support structure, and acting force of hydropneumatic suspensions etc. were obtained from the M&S. Additionally frequency analysis were performed using the data of behavior obtained from above M&S. Generally the quantitative results of RFlex are larger than them of MRBD in view of magnitude of the comparable parametric values.

• Journal of the Korean Dietetic Association
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• v.13 no.2
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• pp.138-156
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• 2007

The purposes of this study were to a) measure the service quality attributes of foodservice type such as school foodservice, hospital foodservice and business & industry(B&I) foodservice, managed by contract management company(CMC), b) compare with service quality attributes by CMC scale, c) analyze overall customer satisfaction(CS) by the foodservice type and the CMC scale, and d) identify the effect of service quality attributes on an overall CS by the foodservice type and the CMC scale. The questionnaires were handed out to 6,620 customers of 207 school, 38 hospital, and 86 B&I foodservices in 108 CMCs. The statistical data analysis was completed using SPSS Win(ver 12.0) for descriptive analysis, t-test, reliability analysis, and multiple linear regression analysis. From an analysis on service quality attributes, 'proper arrangement of table and chair at hall distribution(3.53)', 'operation of nutrition education(3.50)' were highly perceived to student, 'correctable serving(4.08)', 'serve at fixed distribution time(4.08)', 'kindness of serving employee(4.04)' were highly perceived to patient, 'employee's kindness(3.84)' were highly perceived to customer of B&I. In comparison of service quality attributes by CMC scale, most scores of large enterprise(LE) were significantly higher than small and medium sized enterprise(SME) in school foodservice, hospital foodservice and B&I foodservice. Overall CS levels were 3.53 out of a maximum 5 on B&I, 3.46 on school, and 3.44 on hospital and were evaluated differently CS score by CMC scale. Finally, regression results for the effects of service quality attributes on overall CS by each of foodservice type were identified significantly different service quality attributes by foodservice type such as school, hospital, B&I(p<.001) and by CMC scale. For considering the goal of enterprise on profit-making through CS and the needs of customer on CS at moment of truth(MOT), the findings should be applied to the CMC and the foodservice industry.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.77-82
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• 2015

Big Data technology has been attracted much attention in aspect of fast data processing. Research of practicing Big Data technology is also ongoing to process large-scale structured data much faster in Relatioinal Database(RDB). Although there are lots of studies about measuring analyzing performance, studies about structured data loading performance, prior step of analyzing, is very rare. Thus, in this study, structured data in RDB is tested the performance that loads distributed processing platform Hadoop using Apache sqoop. Also in order to analyze the influence factors of data loading, it is tested repeatedly with different options of data loading and compared with data loading performance among RDB based servers. Although data loading performance of Apache Sqoop in test environment was low, but in large-scale Hadoop cluster environment we can expect much better performance because of getting more hardware resources. It is expected to be based on study improving data loading performance and whole steps of performance analyzing structured data in Hadoop Platform.